The present invention relates to digital image processing. In particular, this invention relates to a non-linear and linear method of scaling up or scaling down the resolution of a digital input image to a defined output window.
Digital displays are characterized by their scan rate and pixel resolution. Standard non-interlaced displays have scan rate of at least 60 HZ and resolution of at least 480 lines (rows) by 640 pixels (columns) in each line. Non-interlaced displays are typically formatted as VGA, SVGA, XGA, SXGA and UGA, where each one has a different resolution and scan rate. It often occurs that various display systems are mixed together and transformation from one resolution to other is needed. For example, de-interlaced video images are usually of size 480 lines by 640 pixels resolution (VGA format). Such images are visualized relatively small when displayed on a monitor with pixel resolution capacity of 1200 lines by 1600 pixels. There is a need for a method of scaling up or scaling down the resolution of a digital input image to a defined output window, in order to display a video image over the entire resolution capacity of a display monitor having different resolution from the video image.
One of the most important characterizations of advanced, interlaced or non-interlaced, video screens is the ratio between the width (number of columns) and the height (number of rows) of the actual display area, commonly called the aspect ratio of the display screen. Currently, the most popular aspect ratio is 4:3 (for example, de-interlaced video images of size 480 rows by 640 columns or pixels (VGA format), or 1200 rows by 1600 columns). New screens are now available in the market with aspect ratios such as 16:9 or 21:9. High Definition TV screens typically have an aspect ratio of 16:9. In order to present images of one aspect ratio, for example 4:3, on displays having a different aspect ratio, for example 16:9, a sophisticated transformation that converts the image between aspect ratios is needed.
In such non-linear image resolution conversion cases, where the input and output conversion ratio is not 1:1, i.e., 4:3 ? 16:9, using standard linear resolution conversion methods on digital input images results in distortions within the corresponding output images. Various methods of video image resolution conversion and scaling have been developed, for non-linear or linear cases, most of which either feature or include linear interpolation processing for the purpose of either adding pixels by estimating values of missing pixels, or deleting pixels with known values, in the input image, for producing a converted output image. Using standard interpolation methods for adding missing pixels to an input image as part of forming an output image during scale-up resolution conversion, or for deleting pixels from an input image as part of forming an output image during scale-down resolution conversion, results in an output image either containing additional pixels in, or missing deleted pixels from, the initial input image, according to whether the image resolution conversion is scale-up, or scale-down, respectively. In such methods, input image data is simply used as a template for forming the output image. A more sophisticated method of image resolution conversion, and one in which higher quality results are obtained, is one which includes the formation of an entirely new output image, by calculating each output image pixel, in principle, from scratch, using input image pixels only as a starting point of resolution conversion, and not where all, or portions, of input image data simply become part of the output image. Moreover, many standard linear interpolation methods may not be computationally efficient for producing high quality resolution conversion images.
There is thus a need for a sophisticated, yet computationally efficient, non-linear transformation that minimizes distortions resulting from scale-up or scale-down resolution conversion with non-linear changes in display screen aspect ratios. There is also a need for, and it would be useful to have a sophisticated, yet computationally efficient linear transformation for performing scale-up or scale-down image resolution conversion, in cases where there is no change in aspect ratios between the video input image and the display output image.
Relative suitability of known methods of image resolution conversion ultimately depends on the resulting image quality. Moreover, different methods of image resolution conversion work better under different conditions.
U.S. Pat. No. 5,513,120 issued to Berlad, is based on a four-point linear interpolation method, using nearest neighbor grid points and the next nearest neighbor grid points that are in a line with the grid location point requiring interpolated data, for estimating missing pixels required for conversion of video images. Interpolation involves the use of Lagrange polynomials for determination of four interpolation coefficients and each output pixel value, whereby the texture of the image does not vary as a function of pixel location. The interpolation method can be extended to an n-dimensional display grid.
U.S. Pat. No. 5,574,572 issued to Malinowski et al., describes various configurations of a video scaling method and device featuring a linear interpolator and decimating FIR filter with constant coefficients for horizontal or vertical scaling of video images.
U.S. Pat. No. 5,119,082 issued to Lumelsky et al., features a pixel rate expansion circuit with a linear scaling method for video expansion, along with a means of defining a window as a subset of an entire display and scaling a video image to fit. The circuit includes a linear scaling mechanism, which causes selected adjacent scan lines to be repeated as they are read out of a frame buffer, for vertically and horizontally expanding an input image.
U.S. Pat. No. 5,559,905 issued to Greggain et al., describes a digital image resizing apparatus operating with a linear combination of interpolation filters. Filter coefficients are multiplied by input data and the results are shifted and sign extended to compensate for reduced precision of resizing the image.
U.S. Pat. No. 5,796,879 issued to Wong et al., teaches of using the technique of area-based interpolation for performing image interpolation, emphasizing scaling-up of images. Pixel values are determined from integrals of curves over an area proportional to a sampling size of an input image. Two integrators and two interpolation steps, including the use of a linear filter and evaluation of coefficients by solving linear polynomial equations, are required to achieve the desired image conversion.
U.S. Pat. No. 5,532,716 issued to Sano, describes a resolution conversion system for scaling-down images. The system operates with scaling factors proportional to input and output image sizes, and provides linear image conversion in both horizontal and vertical directions.
U.S. Pat. No. 5,446,831 issued to Yamashita et al., describes an image data processor for scaling-down an image. A base 2 logarithmic expression is used for changing the amount of data required for performing the desired resolution conversion. The image processor converts and reduces binary image data in both vertical and horizontal direction.
Additional methods of digital image resolution conversion include using an error diffusion technique, U.S. Pat. No. 5,208,871 issued to Eschbach, and an area mapping technique using reference clusters of a digitized input image, U.S. Pat. No. 5,758,034 and 5,689,343, issued to Loce et al.